Work

Integration of Machine Learning Methods to Calculate the Remaining Useful Life of Mandrels

Year: 2025

Type: article

Abstract: Predicting the service life of tools offers many advantages in manufacturing, such as reduced maintenance and fewer machine failures. The advances in machine learning methods, representing a sub-area of AI, are constantly creating new potentials in this field. However, process data with a sufficiently high information density are required to use machine learning for service life predictions. In many instances, a primary requirement is to map the entire use of the considered tool. Therefore, this article examines the suitability of an existing process data set of a ring rolling process that maps the whole use of the tool. The considered tools are mandrels, which are used to produce rings in the hot-forming industry. In the first step, the fracture pattern of a mandrel is microstructurally investigated. Subsequently, a machine learning model is developed to examine the suitability of this dataset to predict service life in an initial feasibility study.

Source: Zeitschrift für wirtschaftlichen Fabrikbetrieb

DOI: 10.1515/zwf-2024-0118

Authors: Tobias Moser, Johannes Seitz, Bernd Kuhlenkötter

Institution: Ruhr University Bochum

Language: English

Topic: Reliability and Maintenance Optimization

Subfield: Safety, Risk, Reliability and Quality

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: hybrid

Estimated APC paid: $2,712